Modern data analytics creates vast volumes of data. In the past, analysis tools have performed poorly at identifying anomalies in these vast pools of data. Such anomaly identification is sometimes performed, in-part, with moving average analysis. However, even with moving average analysis the process has still been man-hour intensive and, because of the large volumes of data, it has been easy to get lost down a path of data that may lead to nowhere or is ultimately not interesting. Thus, due in part to the large amounts of data and the large number of associated metrics, data analysts may not even know where to begin their analysis. As such, some analysts become tied to a particular familiar metric simply because they have been otherwise unable to narrow the field of data to what is interesting for the particular set of data or because the industry has chosen a few select metrics by default.
Furthermore, some web analytics data may have a cyclical nature that is poorly suited to moving average analysis. Cyclical behavior may exist for any number of reasons, for example but not limited to, seasonality, periods of time, holidays, etc. Using a running average based upon high volume week-day traffic to search for anomalies in low volume weekend traffic may obtain poor results because an expected range determined by high volume traffic may fail to detect anomalies when applied to actual low volume traffic values, for example.